The probing classifiers framework has been employed for interpreting deep
neural network models for a variety of natural language processing (NLP)
applications. Studies, however, have largely focused on sentencelevel NLP
tasks. This work is the first to apply the probing paradigm to representations
learned for document-level information extraction (IE). We designed eight
embedding probes to analyze surface, semantic, and event-understanding
capabilities relevant to document-level event extraction. We apply them to the
representations acquired by learning models from three different LLM-based
document-level IE approaches on a standard dataset. We found that trained
encoders from these models yield embeddings that can modestly improve argument
detections and labeling but only slightly enhance event-level tasks, albeit
trade-offs in information helpful for coherence and event-type prediction. We
further found that encoder models struggle with document length and
cross-sentence discourse.

将探测分类器框架应用于文本级别信息提取，研究表明，通过深度神经网络模型得到的表示可以在一定程度上改进参数检测和标签化，并略微提升事件级别任务，尽管在连贯性和事件类型预测方面会有一些权衡，同时在处理文件长度和跨句子语篇方面仍存在一定的困难。

对文档级事件抽取的表示探测

Probing Representations for Document-level Event Extraction

Document-level information extraction (IE) is a crucial task in natural
language processing (NLP). This paper conducts a systematic review of recent
document-level IE literature. In addition, we conduct a thorough error analysis
with current state-of-the-art algorithms and identify their limitations as well
as the remaining challenges for the task of document-level IE. According to our
findings, labeling noises, entity coreference resolution, and lack of
reasoning, severely affect the performance of document-level IE. The objective
of this survey paper is to provide more insights and help NLP researchers to
further enhance document-level IE performance.

这篇综述研究对最近的文档级信息抽取文献进行了系统回顾，通过与当前最先进的算法进行彻底的错误分析，确定它们的局限性以及文档级信息抽取任务的剩余挑战，包括标签误差、实体关联解析和缺乏推理，严重影响文档级信息抽取的性能。本综述的目标是为 NLP 研究人员提供更多见解，帮助进一步提高文档级信息抽取的性能。